A Novel Method for Plant Leaf Malady Recognition using Machine Learning Classifiers

Swetha, V.; Jayaram, Rekha

doi:10.1109/iceca.2019.8822094

Cited by 5 publications

(3 citation statements)

References 7 publications

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“…In the literature, methods based on machine learning have been found to detect diseases on agricultural plants. In [6], the authors emphasized automated methods for early detection of the disease on various plants. They used an image dataset of various plants containing images of healthy and affected leaves.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Therefore, compared with traditional methods, the time of physical analysis is saved and higher accuracy is provided. Researchers are trying to improve these methods by improving the accuracy of early disease detection [6]. In addition, these advanced technologies also contribute to precision agriculture, which is a technology that uses drones to spray pesticides in specific areas.…”

Section: Introductionmentioning

confidence: 99%

“…Through deep learning methods involving multiple levels of complexity, we can simply distinguish healthy plants from affected plants with high accuracy. We can also change the parameters of each layer and find the ideal parameter settings to achieve accuracy [6].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Leaf Blights Detection and Classification in Large Scale Applications

Fayyaz¹,

Al‐Dhlan²,

Rehman³

et al. 2022

Intelligent Automation &Amp; Soft Computing

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Crops are very important to the financial needs of a country. Due to various diseases caused by different pathogens, a large number of crops have been destroyed. As humanoids, our basic need is food for survival, and the most basic foundation of our food is agriculture. For many developing countries, it is mainly an important source of income. Bacterial diseases are one of the main diseases that cause improper production and a major economic crisis for the country. Therefore, it is necessary to detect the disease early. However, it is not easy for humans to analyze the different leaves of plants by themselves when recognizing diseases. In this article, a variety of machine learning methods are used to classify and detect leaf blight. We use the fusion of deep convolutional neural network (CNN) models obtained from SqueezeNet and ShuffleNet to improve the accuracy and robustness of large-scale applications. We use entropy to reduce the complexity of the calculation process and reduce the features in the deep learning process. In addition, we use a support vector machine (SVM) classifier to obtain the classification. We use the CIELAB color space to capture the entire color range to improve accuracy. Our results are very promising because we have achieved 98% accuracy in the early detection of leaf blight.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%